CSE 140 wrapup

1. CSE 140 wrapup Michael Ernst CSE 140 University of Washington

2. Progress in 10 weeks 10 weeks ago: you knew no programming Goals: • Computational problem-solving • Pythonprogramming language • Experience withreal datasets • Fun of extracting understanding and insight from data, and of mastery over the computer • Ability to go on to more advanced computing classes Today: you can write a useful program to solve a real problem • You can even pose the problem yourself

3. Example from lecture 0:Assessing treatment efficacy Zip code of clinic number of follow ups within 16 weeks after treatment enrollment. Zip code of patient Question: Does the distance between the patient’s home and clinic influence the number of follow ups, and therefore treatment efficacy?

4. Python program to assess treatment efficacy # This program reads an Excel spreadsheet whose penultimate # and antepenultimate columns are zip codes. # It adds a new last column for the distance between those zip #codes, and outputs in CSV (comma-separated values) format. # Call the program with two numeric values: the first and last # row to include. # The output contains the column headers and those rows. # Libraries to use importrandom import sys importxlrd# library for working with Excel spreadsheets import time fromgdapiimportGoogleDirections # No key needed if few queries gd= GoogleDirections('dummy-Google-key') wb= xlrd.open_workbook('mhip_zip_eScience_121611a.xls') sheet = wb.sheet_by_index(0) # User input: first row to process, first row not to process first_row = max(int(sys.argv[1]), 2) row_limit= min(int(sys.argv[2]+1), sheet.nrows) defcomma_separated(lst): return ",".join([str(s) for s inlst]) headers = sheet.row_values(0) + ["distance"] printcomma_separated(headers) forrownuminrange(first_row,row_limit): row = sheet.row_values(rownum) (zip1, zip2) = row[-3:-1] if zip1 and zip2: # Clean the data zip1 = str(int(zip1)) zip2 = str(int(zip2)) row[-3:-1] = [zip1, zip2] # Compute the distance via Google Maps try: distance = gd.query(zip1,zip2).distance except: print >> sys.stderr, "Error computing distance:", zip1, zip2 distance = "" # Print the row with the distance printcomma_separated(row + [distance]) # Avoid too many Google queries in rapid succession time.sleep(random.random()+0.5) 23 lines of executable code!

5. A huge thanks to the rest of the staff Dun-Yu Hsiao

6. Why do you care about processing data? • The world is awash in data • Processing and analyzing it is the difference between success and failure • for a team or for an individual • Manipulating and understanding data is essential to: • Astronomers • Biologists • Chemists • Economists • Engineers • Entrepreneurs • Linguists • Political scientists • Zoologists • … and many more!

7. Why the Python language?

8. Comparison of Python with Java • Python is better for learning programming • Python is better for small prograams • Java is better for large programs Main difference: dynamic vs. static typing • Dynamic typing: put anything in any variable • Static typing: • Source code states the type of the variable • Cannot run code if any assignment might violate the type

9. Python concepts • Assignments • Variables • Types • Programs & algorithms • Control flow: loops (for), conditionals (if) • File I/O • Python execution model • How Python evaluates expressions, statements, and programs

10. Data structures: managing data • List • Set • Dictionary • Tuple • Graph • List slicing (sublist) • List comprehension: shorthand for a loop • Mutable and immutable data structures • Immutable: easier to reason about, less efficient • Distinction between identity and value

11. f(x) = x2 Functions • Procedural abstraction • avoid duplicated code • the implementation does not matter to the client • Using functions • Defining functions • A function is an ordinary value • assign to variables • in a call, use an expression as the function: myfns[i](arg) • Method syntax: put first argument before a period (.) • arg1.methodname(arg2, arg3) • used for “objects” • (period also means “look up variable in a namespace”)

12. Data abstraction Dual to procedural abstraction (functions) A module is: operations An object is: data + operations Operations: create, query, modify Clients use the operations, never directly access data The representation of the data does not matter Programmer defines a class.Each instance of a class is an object.

13. Testing and debugging Write enough tests: • Cover every branch of each boolean expression • especially when used in a conditional expression (if statement) • Cover special cases: • numbers: zero, positive, negative, int vs. float • data structures: empty, size 1, larger Assertions are useful beyond tests Debugging: after you observe a failure • Divide and conquer • In time, in data, in program text, in development history • this is also a key program design concept • The scientific method • state a hypothesis; design an experiment; understand results Think first • Be systematic: record everything; have a reason for each action

14. Data analysis Statistics • Run many simulations • How uncommon is what you actually saw? Graphing/plotting results

15. Program design How to write a function: • Name, arguments, and documentation string • Tests • Body/implementation How to write a program: • Decompose into parts (functions, modules) • Each part should be a logical unit, not too large or small • Write each part • Define the problem • Choose an algorithm • In English first; test it via manual simulation • Translate into code When necessary, use wishful thinking • Assume a function exists, then write it later • Can test even before you write it, via a stub

16. Recursion • Base case: does all the work for a small problem • Inductive case: • passes the buck for most of a large problem • does a small amount of work (or none) to the subanswer • returns whole result

17. Speed of algorithms Affected primarily by the number of times you iterate over data “Constant factors” don’t matter (looping 2 times or 3 times) Nested looping matters a lot

18. There is more to learn • Data analysis, data science, and data visualization • Scaling up: • Larger and more complex programs • “Big data”: out-of-memory data, parallel programming, … • Ensuring correctness • Principled, systematic design, testing, and programming • Coding style • Managing complexity • Programming tools: testing, version control, debugging, deployment • GUIs, user interaction • Data structures and algorithms • Working in a team

19. What you have learned in CSE 140 Compare your skills today to 10 weeks ago Theory: abstraction, specification, design Practice: implementation, testing Bottom line: The assignments would be easy for you today This is a measure of how much you have learned There is no such thing as a “born” programmer! Your next project can be more ambitious Genius is 1% inspiration and 99% perspiration. Thomas A. Edison

20. What you will learn later Your next project can be much more ambitious Know your limits Be humble (reality helps you with this) You will continue to learn Building interesting systems is never easy Like any worthwhile endeavor Practice is a good teacher Requires thoughtful introspection Don’t learn only by trial and error! Get lots of practice and feedback

21. What comes next? Classes • Java: CSE 142 (you might skip), CSE 143, CSE 143X • HDCE 310: Python for Internet mashups • MATLAB, other programming languages • Self-study: books & websites Data analysis: classes, research, jobs • In programming and software engineering • In any topic that involves software Having an impact on the world • Jobs (and job interviews) • Larger programming projects The purpose of computing is insight, not numbers. Richard W. Hamming Numerical Methods for Scientists and Engineers

22. Go forth and conquer System building and scientific discovery are fun! It’s even more fun when your system works Pay attention to what matters Use the techniques and tools of CSE 140 effectively